# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utility functions for GLUE classification tasks."""

import collections
import csv
import os
from bert import modeling
from bert import optimization
from bert import tokenization
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator as tf_estimator
from tensorflow.contrib import tpu as contrib_tpu


class InputExample:
  """A single training/test example for simple sequence classification."""

  def __init__(self, guid, text_a, text_b=None, label=None):
    """Constructs a InputExample.

    Args:
      guid: Unique id for the example.
      text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
      text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
      label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """
    self.guid = guid
    self.text_a = text_a
    self.text_b = text_b
    self.label = label


class PaddingInputExample:
  """Fake example so the num input examples is a multiple of the batch size.

  When running eval/predict on the TPU, we need to pad the number of examples
  to be a multiple of the batch size, because the TPU requires a fixed batch
  size. The alternative is to drop the last batch, which is bad because it means
  the entire output data won't be generated.

  We use this class instead of `None` because treating `None` as padding
  battches could cause silent errors.
  """


class InputFeatures:
  """A single set of features of data."""

  def __init__(self,
               input_ids,
               input_mask,
               segment_ids,
               label_id,
               guid=None,
               example_id=None,
               is_real_example=True):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.label_id = label_id
    self.example_id = example_id
    self.guid = guid
    self.is_real_example = is_real_example


class DataProcessor:
  """Base class for data converters for sequence classification data sets."""

  def __init__(self, use_spm, do_lower_case):
    super().__init__()
    self.use_spm = use_spm
    self.do_lower_case = do_lower_case

  def get_train_examples(self, data_dir):
    """Gets a collection of `InputExample`s for the train set."""
    raise NotImplementedError()

  def get_dev_examples(self, data_dir):
    """Gets a collection of `InputExample`s for the dev set."""
    raise NotImplementedError()

  def get_test_examples(self, data_dir):
    """Gets a collection of `InputExample`s for prediction."""
    raise NotImplementedError()

  def get_labels(self):
    """Gets the list of labels for this data set."""
    raise NotImplementedError()

  @classmethod
  def _read_tsv(cls, input_file, quotechar=None):
    """Reads a tab separated value file."""
    with tf.gfile.Open(input_file, "r") as f:
      reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
      lines = []
      for line in reader:
        lines.append(line)
      return lines

  def process_text(self, text):
    if self.use_spm:
      return tokenization.preprocess_text(text, self.do_lower_case)
    else:
      return tokenization.convert_to_unicode(text)


class MnliProcessor(DataProcessor):
  """Processor for the MultiNLI data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MNLI", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MNLI", "dev_matched.tsv")),
        "dev_matched")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MNLI", "test_matched.tsv")),
        "test")

  def get_labels(self):
    """See base class."""
    return ["contradiction", "entailment", "neutral"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      # Note(mingdachen): We will rely on this guid for GLUE submission.
      guid = self.process_text(line[0])
      text_a = self.process_text(line[8])
      text_b = self.process_text(line[9])
      if set_type == "test":
        label = "contradiction"
      else:
        label = self.process_text(line[-1])
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class MisMnliProcessor(MnliProcessor):
  """Processor for the Mismatched MultiNLI data set (GLUE version)."""

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MNLI", "dev_mismatched.tsv")),
        "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MNLI", "test_mismatched.tsv")),
        "test")


class MrpcProcessor(DataProcessor):
  """Processor for the MRPC data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MRPC", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MRPC", "dev.tsv")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "MRPC", "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return ["0", "1"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = "%s-%s" % (set_type, i)
      text_a = self.process_text(line[3])
      text_b = self.process_text(line[4])
      if set_type == "test":
        guid = line[0]
        label = "0"
      else:
        label = self.process_text(line[0])
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class ColaProcessor(DataProcessor):
  """Processor for the CoLA data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "CoLA", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "CoLA", "dev.tsv")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "CoLA", "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return ["0", "1"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      # Only the test set has a header
      if set_type == "test" and i == 0:
        continue
      guid = "%s-%s" % (set_type, i)
      if set_type == "test":
        guid = line[0]
        text_a = self.process_text(line[1])
        label = "0"
      else:
        text_a = self.process_text(line[3])
        label = self.process_text(line[1])
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
    return examples


class Sst2Processor(DataProcessor):
  """Processor for the SST-2 data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "SST-2", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "SST-2", "dev.tsv")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "SST-2", "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return ["0", "1"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      if set_type != "test":
        guid = "%s-%s" % (set_type, i)
        text_a = self.process_text(line[0])
        label = self.process_text(line[1])
      else:
        guid = self.process_text(line[0])
        # guid = "%s-%s" % (set_type, line[0])
        text_a = self.process_text(line[1])
        label = "0"
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
    return examples


class StsbProcessor(DataProcessor):
  """Processor for the STS-B data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "STS-B", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "STS-B", "dev.tsv")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "STS-B", "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return [None]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = self.process_text(line[0])
      # guid = "%s-%s" % (set_type, line[0])
      text_a = self.process_text(line[7])
      text_b = self.process_text(line[8])
      if set_type != "test":
        label = float(line[-1])
      else:
        label = 0
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class QqpProcessor(DataProcessor):
  """Processor for the QQP data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "QQP", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "QQP", "dev.tsv")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "QQP", "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return ["0", "1"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = line[0]
      # guid = "%s-%s" % (set_type, line[0])
      if set_type != "test":
        try:
          text_a = self.process_text(line[3])
          text_b = self.process_text(line[4])
          label = self.process_text(line[5])
        except IndexError:
          continue
      else:
        text_a = self.process_text(line[1])
        text_b = self.process_text(line[2])
        label = "0"
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class QnliProcessor(DataProcessor):
  """Processor for the QNLI data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "QNLI", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "QNLI", "dev.tsv")),
        "dev_matched")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "QNLI", "test.tsv")),
        "test_matched")

  def get_labels(self):
    """See base class."""
    return ["entailment", "not_entailment"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = self.process_text(line[0])
      # guid = "%s-%s" % (set_type, line[0])
      text_a = self.process_text(line[1])
      text_b = self.process_text(line[2])
      if set_type == "test_matched":
        label = "entailment"
      else:
        label = self.process_text(line[-1])
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class RteProcessor(DataProcessor):
  """Processor for the RTE data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "RTE", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "RTE", "dev.tsv")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "RTE", "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return ["entailment", "not_entailment"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = self.process_text(line[0])
      # guid = "%s-%s" % (set_type, line[0])
      text_a = self.process_text(line[1])
      text_b = self.process_text(line[2])
      if set_type == "test":
        label = "entailment"
      else:
        label = self.process_text(line[-1])
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class WnliProcessor(DataProcessor):
  """Processor for the WNLI data set (GLUE version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "WNLI", "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "WNLI", "dev.tsv")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "WNLI", "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return ["0", "1"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = self.process_text(line[0])
      # guid = "%s-%s" % (set_type, line[0])
      text_a = self.process_text(line[1])
      text_b = self.process_text(line[2])
      if set_type != "test":
        label = self.process_text(line[-1])
      else:
        label = "0"
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class AXProcessor(DataProcessor):
  """Processor for the AX data set (GLUE version)."""

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "diagnostic", "diagnostic.tsv")),
        "test")

  def get_labels(self):
    """See base class."""
    return ["contradiction", "entailment", "neutral"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      # Note(mingdachen): We will rely on this guid for GLUE submission.
      guid = self.process_text(line[0])
      text_a = self.process_text(line[1])
      text_b = self.process_text(line[2])
      if set_type == "test":
        label = "contradiction"
      else:
        label = self.process_text(line[-1])
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples


class LCQMCPairClassificationProcessor(DataProcessor):
  """Processor for the internal data set. sentence pair classification."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "train.txt")), "train")
    # dev_0827.tsv

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "test.txt")), "dev")

  def get_test_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "test.txt")), "test")

  def get_labels(self):
    """See base class."""
    return ["0", "1"]

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    print("length of lines:", len(lines))
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = "%s-%s" % (set_type, i)
      try:
        label = tokenization.convert_to_unicode(line[2])
        text_a = tokenization.convert_to_unicode(line[0])
        text_b = tokenization.convert_to_unicode(line[1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      except Exception:  # pylint: disable=broad-except
        print("###error.i:", i, line)
    return examples


def convert_single_example(ex_index, example, label_list, max_seq_length,
                           tokenizer, task_name):
  """Converts a single `InputExample` into a single `InputFeatures`."""

  if isinstance(example, PaddingInputExample):
    return InputFeatures(
        input_ids=[0] * max_seq_length,
        input_mask=[0] * max_seq_length,
        segment_ids=[0] * max_seq_length,
        label_id=0,
        is_real_example=False)

  if task_name != "sts-b":
    label_map = {}
    for (i, label) in enumerate(label_list):
      label_map[label] = i

  tokens_a = tokenizer.tokenize(example.text_a)
  tokens_b = None
  if example.text_b:
    tokens_b = tokenizer.tokenize(example.text_b)

  if tokens_b:
    # Modifies `tokens_a` and `tokens_b` in place so that the total
    # length is less than the specified length.
    # Account for [CLS], [SEP], [SEP] with "- 3"
    _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
  else:
    # Account for [CLS] and [SEP] with "- 2"
    if len(tokens_a) > max_seq_length - 2:
      tokens_a = tokens_a[0:(max_seq_length - 2)]

  # The convention in BERT is:
  # (a) For sequence pairs:
  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
  #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
  # (b) For single sequences:
  #  tokens:   [CLS] the dog is hairy . [SEP]
  #  type_ids: 0     0   0   0  0     0 0
  #
  # Where "type_ids" are used to indicate whether this is the first
  # sequence or the second sequence. The embedding vectors for `type=0` and
  # `type=1` were learned during pre-training and are added to the wordpiece
  # embedding vector (and position vector). This is not *strictly* necessary
  # since the [SEP] token unambiguously separates the sequences, but it makes
  # it easier for the model to learn the concept of sequences.
  #
  # For classification tasks, the first vector (corresponding to [CLS]) is
  # used as the "sentence vector". Note that this only makes sense because
  # the entire model is fine-tuned.
  tokens = []
  segment_ids = []
  tokens.append("[CLS]")
  segment_ids.append(0)
  for token in tokens_a:
    tokens.append(token)
    segment_ids.append(0)
  tokens.append("[SEP]")
  segment_ids.append(0)

  if tokens_b:
    for token in tokens_b:
      tokens.append(token)
      segment_ids.append(1)
    tokens.append("[SEP]")
    segment_ids.append(1)

  input_ids = tokenizer.convert_tokens_to_ids(tokens)

  # The mask has 1 for real tokens and 0 for padding tokens. Only real
  # tokens are attended to.
  input_mask = [1] * len(input_ids)

  # Zero-pad up to the sequence length.
  while len(input_ids) < max_seq_length:
    input_ids.append(0)
    input_mask.append(0)
    segment_ids.append(0)

  assert len(input_ids) == max_seq_length
  assert len(input_mask) == max_seq_length
  assert len(segment_ids) == max_seq_length

  if task_name != "sts-b":
    label_id = label_map[example.label]
  else:
    label_id = example.label

  if ex_index < 5:
    tf.logging.info("*** Example ***")
    tf.logging.info("guid: %s" % (example.guid,))
    tf.logging.info("tokens: %s" %
                    " ".join([tokenization.printable_text(x) for x in tokens]))
    tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
    tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
    tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
    tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

  feature = InputFeatures(
      input_ids=input_ids,
      input_mask=input_mask,
      segment_ids=segment_ids,
      label_id=label_id,
      is_real_example=True)
  return feature


def file_based_convert_examples_to_features(examples, label_list,
                                            max_seq_length, tokenizer,
                                            output_file, task_name):
  """Convert a set of `InputExample`s to a TFRecord file."""

  writer = tf.python_io.TFRecordWriter(output_file)

  for (ex_index, example) in enumerate(examples):
    if ex_index % 10000 == 0:
      tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

    feature = convert_single_example(ex_index, example, label_list,
                                     max_seq_length, tokenizer, task_name)

    def create_int_feature(values):
      f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
      return f

    def create_float_feature(values):
      f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
      return f

    features = collections.OrderedDict()
    features["input_ids"] = create_int_feature(feature.input_ids)
    features["input_mask"] = create_int_feature(feature.input_mask)
    features["segment_ids"] = create_int_feature(feature.segment_ids)
    features["label_ids"] = create_float_feature([feature.label_id])\
        if task_name == "sts-b" else create_int_feature([feature.label_id])
    features["is_real_example"] = create_int_feature(
        [int(feature.is_real_example)])

    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
    writer.write(tf_example.SerializeToString())
  writer.close()


def file_based_input_fn_builder(input_file,
                                seq_length,
                                is_training,
                                drop_remainder,
                                task_name,
                                use_tpu,
                                bsz,
                                multiple=1):
  """Creates an `input_fn` closure to be passed to TPUEstimator."""
  labeltype = tf.float32 if task_name == "sts-b" else tf.int64

  name_to_features = {
      "input_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64),
      "input_mask": tf.FixedLenFeature([seq_length * multiple], tf.int64),
      "segment_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64),
      "label_ids": tf.FixedLenFeature([], labeltype),
      "is_real_example": tf.FixedLenFeature([], tf.int64),
  }

  def _decode_record(record, name_to_features):
    """Decodes a record to a TensorFlow example."""
    example = tf.parse_single_example(record, name_to_features)

    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
    # So cast all int64 to int32.
    for name in list(example.keys()):
      t = example[name]
      if t.dtype == tf.int64:
        t = tf.to_int32(t)
      example[name] = t

    return example

  def input_fn(params):
    """The actual input function."""
    if use_tpu:
      batch_size = params["batch_size"]
    else:
      batch_size = bsz

    # For training, we want a lot of parallel reading and shuffling.
    # For eval, we want no shuffling and parallel reading doesn't matter.
    d = tf.data.TFRecordDataset(input_file)
    if is_training:
      d = d.repeat()
      d = d.shuffle(buffer_size=100)

    d = d.apply(
        tf.data.experimental.map_and_batch(
            lambda record: _decode_record(record, name_to_features),
            batch_size=batch_size,
            drop_remainder=drop_remainder))

    return d

  return input_fn


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
  """Truncates a sequence pair in place to the maximum length."""

  # This is a simple heuristic which will always truncate the longer sequence
  # one token at a time. This makes more sense than truncating an equal percent
  # of tokens from each, since if one sequence is very short then each token
  # that's truncated likely contains more information than a longer sequence.
  while True:
    total_length = len(tokens_a) + len(tokens_b)
    if total_length <= max_length:
      break
    if len(tokens_a) > len(tokens_b):
      tokens_a.pop()
    else:
      tokens_b.pop()


def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                 labels, num_labels, use_one_hot_embeddings, task_name):
  """Creates a classification model."""
  model = modeling.BertModel(
      config=bert_config,
      is_training=is_training,
      input_ids=input_ids,
      input_mask=input_mask,
      token_type_ids=segment_ids,
      use_one_hot_embeddings=use_one_hot_embeddings)

  # In the demo, we are doing a simple classification task on the entire
  # segment.
  #
  # If you want to use the token-level output, use model.get_sequence_output()
  # instead.
  output_layer = model.get_pooled_output()

  hidden_size = output_layer.shape[-1].value

  output_weights = tf.get_variable(
      "output_weights", [num_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.get_variable(
      "output_bias", [num_labels], initializer=tf.zeros_initializer())

  with tf.variable_scope("loss"):
    if is_training:
      # I.e., 0.1 dropout
      output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

    logits = tf.matmul(output_layer, output_weights, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias)
    if task_name != "sts-b":
      probabilities = tf.nn.softmax(logits, axis=-1)
      predictions = tf.argmax(probabilities, axis=-1, output_type=tf.int32)
      log_probs = tf.nn.log_softmax(logits, axis=-1)
      one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)

      per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
    else:
      probabilities = logits
      logits = tf.squeeze(logits, [-1])
      predictions = logits
      per_example_loss = tf.square(logits - labels)
    loss = tf.reduce_mean(per_example_loss)

    return (loss, per_example_loss, probabilities, logits, predictions)


def model_fn_builder(bert_config,
                     num_labels,
                     init_checkpoint,
                     learning_rate,
                     num_train_steps,
                     num_warmup_steps,
                     use_tpu,
                     use_one_hot_embeddings,
                     task_name,
                     optimizer="adamw"):
  """Returns `model_fn` closure for TPUEstimator."""

  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
    """The `model_fn` for TPUEstimator."""

    tf.logging.info("*** Features ***")
    for name in sorted(features.keys()):
      tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

    input_ids = features["input_ids"]
    input_mask = features["input_mask"]
    segment_ids = features["segment_ids"]
    label_ids = features["label_ids"]
    is_real_example = None
    if "is_real_example" in features:
      is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
    else:
      is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)

    is_training = (mode == tf_estimator.ModeKeys.TRAIN)

    (total_loss, per_example_loss, probabilities, logits, predictions) = \
        create_model(bert_config, is_training, input_ids, input_mask,
                     segment_ids, label_ids, num_labels,
                     use_one_hot_embeddings, task_name)

    tvars = tf.trainable_variables()
    initialized_variable_names = {}
    scaffold_fn = None
    if init_checkpoint:
      (assignment_map, initialized_variable_names
      ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
      if use_tpu:

        def tpu_scaffold():
          tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
          return tf.train.Scaffold()

        scaffold_fn = tpu_scaffold
      else:
        tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

    tf.logging.info("**** Trainable Variables ****")
    for var in tvars:
      init_string = ""
      if var.name in initialized_variable_names:
        init_string = ", *INIT_FROM_CKPT*"
      tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                      init_string)

    output_spec = None
    if mode == tf_estimator.ModeKeys.TRAIN:

      train_op = optimization.create_optimizer(total_loss, learning_rate,
                                               num_train_steps,
                                               num_warmup_steps, use_tpu,
                                               optimizer)

      output_spec = contrib_tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          train_op=train_op,
          scaffold_fn=scaffold_fn)
    elif mode == tf_estimator.ModeKeys.EVAL:
      if task_name not in ["sts-b", "cola"]:

        def metric_fn(per_example_loss, label_ids, logits, is_real_example):
          predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
          accuracy = tf.metrics.accuracy(
              labels=label_ids,
              predictions=predictions,
              weights=is_real_example)
          loss = tf.metrics.mean(
              values=per_example_loss, weights=is_real_example)
          return {
              "eval_accuracy": accuracy,
              "eval_loss": loss,
          }
      elif task_name == "sts-b":

        def metric_fn(per_example_loss, label_ids, logits, is_real_example):
          """Compute Pearson correlations for STS-B."""
          # Display labels and predictions
          concat1 = tf.contrib.metrics.streaming_concat(logits)
          concat2 = tf.contrib.metrics.streaming_concat(label_ids)

          # Compute Pearson correlation
          pearson = tf.contrib.metrics.streaming_pearson_correlation(
              logits, label_ids, weights=is_real_example)

          # Compute MSE
          # mse = tf.metrics.mean(per_example_loss)
          mse = tf.metrics.mean_squared_error(
              label_ids, logits, weights=is_real_example)

          loss = tf.metrics.mean(
              values=per_example_loss, weights=is_real_example)

          return {
              "pred": concat1,
              "label_ids": concat2,
              "pearson": pearson,
              "MSE": mse,
              "eval_loss": loss,
          }
      elif task_name == "cola":

        def metric_fn(per_example_loss, label_ids, logits, is_real_example):
          """Compute Matthew's correlations for STS-B."""
          predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
          # https://en.wikipedia.org/wiki/Matthews_correlation_coefficient
          tp, tp_op = tf.metrics.true_positives(
              predictions, label_ids, weights=is_real_example)
          tn, tn_op = tf.metrics.true_negatives(
              predictions, label_ids, weights=is_real_example)
          fp, fp_op = tf.metrics.false_positives(
              predictions, label_ids, weights=is_real_example)
          fn, fn_op = tf.metrics.false_negatives(
              predictions, label_ids, weights=is_real_example)

          # Compute Matthew's correlation
          mcc = tf.div_no_nan(
              tp * tn - fp * fn,
              tf.pow((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn), 0.5))

          # Compute accuracy
          accuracy = tf.metrics.accuracy(
              labels=label_ids,
              predictions=predictions,
              weights=is_real_example)

          loss = tf.metrics.mean(
              values=per_example_loss, weights=is_real_example)

          return {
              "matthew_corr": (mcc, tf.group(tp_op, tn_op, fp_op, fn_op)),
              "eval_accuracy": accuracy,
              "eval_loss": loss,
          }

      eval_metrics = (metric_fn,
                      [per_example_loss, label_ids, logits, is_real_example])
      output_spec = contrib_tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics,
          scaffold_fn=scaffold_fn)
    else:
      output_spec = contrib_tpu.TPUEstimatorSpec(
          mode=mode,
          predictions={
              "probabilities": probabilities,
              "predictions": predictions
          },
          scaffold_fn=scaffold_fn)
    return output_spec

  return model_fn
